Welcome

This document contains some simple examples for how to use the advanced filtering options in https://plae.nei.nih.gov

Gene Focused

If you are curious about a gene, then there are several ways you can learn about its retinal cell type expression patterning. We will use ATOH7, a transcription factor that regulates retinal ganglion development as our example gene.

UMAP - Table

The UMAP view is a two dimensional representation of the individual cells in the scEiaD. Cells that are closer together have more related gene expression profiles (and thus are likely to be similar cell types).

Let’s go the UMAP - Table viewer in plae:

Viz -> UMAP - Tables

Gray are cells which have no detectable ATOH7.

Yellow is the highest expression. Dark blue the lowest (notice the legend).

Show highest expressing cells

What if we want to see which cells have the highest expression? We can use the “Filter Gene Expression” slider to only show cells with expression above a log2(expression) value.

We see that the highest expressing cells are in the “center” before the branching happens.

Species Filtering

By default plae shows data for all organisms in the database (human, mouse, macaque).

If we only want to see ATOH7 expression in human data, then that is very easy with the powerful “Scatter Filter Category” and “Gene Filter On” sections.

Table Information

While the UMAP view is cool looking, it can’t show you everything….what if we want to know what kind of cells are expressing ATOH7?

We can have quantified information on where ATOH7 is expressed by Cell Type (predicted) (this is our machine learned cell type labels) and organism.

We see that about 50% of the mouse and human neurogenic cell type express ATOH7. In raw counts that is 9173 of 170101 total mouse neurogenic cells. They have an average expression of 6.86. You can sort or filter the table based on queries. If you wanted to see ATOH7 expression in the RGCs this is trivial to do by typing in the box below.

This shows us that ATOH7 expression seems to be dropping in the maturing/mature RGCs (and is much lower in the macaque) relative to the neurogenic population.

Study filtering

As scEiaD is constructed from publicly available datasets, you can also filter the data to only show results from a specific paper. This may be useful if you using the results from that paper and want to check or confirm a finding.

You can see information about the papers / studies in scEiaD by using the adjacent “Make Meta Table” section as follows:

We see that the Clark et al. 2019 study did Smart-seq2 and 10X across many developmental time points in mouse. They study_accession ID is SRP158081. We can use this ID to look at ATOH7 expression only in this study in both the UMAP view and the table view

Expression Plot

As we have a huge number of studies and samples, we can use this (for single cell data) unusual view: a boxplot! We can see how ATOH7 expression changes across celltype and study.

How do we get here?

Make a plot….that shows nothing?

We’ve entered ATOH7 as the gene to plot (1). We are faceting (splitting the plot into separate sub-plots) on Cell Type (predict) (2). We are coloring the data points by study_accession (each study’s average gene expression across the Cell Type (predict) is plotted separately) (3). But we see … nothing. Why?

That is because the Plot Height (400) is not high enough. The text is prioritized over the data, so they are hidden. As it is extremely difficult to “auto” pick the correct height, it was more straightforward to have the user pick it. Usually a value of 1000 will give a reasonable view.

So yes, now we can see the data.

Some cones have ATOH7 expression?

So each point is an independent study. We see high ATOH7 expression in the neurogenic population. But we also see some of the Cones with ATOH7 expression. The legend shows which colors correspond to which study.

One of the studies is a bright purple…probably SRP200599. We can confirm that by replotting the data with a filter that only shows study_accessionSRP200599.

Yep, that is it. We can jump to the UMAP - Table view to pull up the metadata we have extracted about the study.

This is from Buenaventura et al.  and is a study that enriched early mouse cones.

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